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Beamformer Source Estimation Improves Accuracy of Motor-Imagery-based Brain Computer Interface
(2018-01-01)
Chan, Hui-Ling
;
Wang, Jung-Wei
;
Chen, Yong-Sheng
;
交大名義發表
;
分子醫學與生物工程研究所
Motor-imagery-based brain computer interface has advantages in high information transition rate and has potential to achieve high portability without using a display for visual stimulation. However, electroencephalographic data is a mixture of neural activity from various brain locations and also external artifacts. This paper presents a novel method utilizing source level features computed based on maximum contrast beamformer. The estimated source activity has the maximum contrast between the power during the reference period and the period that event-related synchronization or desynchronization emerges. Moreover, this method utilizes the classification structure based on divide-and-conquer concept to identify four classes of motor imageries, including left hand, right hand, tongue, and foot. The experimental results demonstrate the improvement in classification accuracy by using the proposed method.
Assessment of Quantitative Magnetoencephalography using Cross-frequency Phase-phase Coupling
(2018-01-01)
Lee, Pin-Shiuan
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Chen, Yong-Sheng
;
Chan, Hui-Ling
;
Chen, Li-Fen
;
資訊工程學系
Cross-frequency phase-phase coupling (n:m CFC) within a cortical region has been proposed as a potential mechanism to regulate neural communication between different temporal scales. This study investigated n:m CFC values between high and low frequencies in resting-state networks using magnetoencephalographic (MEG) data. Simulation experiments with different frequency pairs were first conducted to evaluate the effect of SNR on the estimation of phase and CFC. The values of n:m CFC between each pair of delta, theta, alpha, beta, and gamma frequencies were estimated from eye-open resting-state MEG data. The topographic maps of CFC values between alpha and other frequencies were illustrated. The results of simulation data showed the increased accuracy of phase and n:m CFC estimation for balanced power of two targeted frequencies. Results of MEG data exhibited that CFC maps of low-alpha activity presented broadly distributed coupling with high frequencies (including beta and gamma) in the frontal regions of salient ventral attention and frontoparietal control networks, whereas high-alpha activity coupling with the gamma band in the posterior part of frontoparietal control network. Our study demonstrates the versatile of n:m CFC values at rest in human brain. Caution should be considered when n:m CFC values would be used as an index of dysfunction at resting state in diseased brains.
Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition
(2018-10-09)
Chan, Hui-Ling
;
Kuo, Po-Chih
;
Cheng, Chia-Yi
;
Chen, Yong-Sheng
;
交大名義發表
The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.
Beamformer-based Imaging of Phase-Amplitude Coupling using Electromagnetic Brain Activity
(2015)
Chan, Hui-Ling
;
Chen, Yong-Sheng
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Chen, Li-Fen
;
Baillet, Sylvain
;
資訊工程學系
Phase-amplitude coupling (PAC) between neural oscillations of different frequencies plays a crucial role in cognitive processing. Assessing the PAC at both sensor and source levels may encounter the problem of spurious coupling because of the volume conduction, field spread, and source leakage. This paper presents a novel method, beamformer-based imaging of PAC (BIPAC), to estimate PAC between sources from electromagnetic signals. For each targeted brain region, this method can extract the source component with the maximum PAC to the reference signal. The results from two simulated MEG data sets demonstrated that the proposed method can achieve high localization accuracy and low spurious coupling.
Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals
(2015-07-01)
Chan, Hui-Ling
;
Chen, Li-Fen
;
Chen, I-Tzu
;
Chen, Yong-Sheng
;
分子醫學與生物工程研究所
Functional connectivity calculated using multiple channels of electromagnetic brain signals is often over- or underestimated due to volume conduction or field spread. Considering connectivity measures, coherence is suitable for the detection of rhythmic synchronization, whereas temporal correlation is appropriate for transient synchronization. This paper presents a beamformer-based imaging method, called spatiotemporal imaging of linearly-related source component (SILSC), which is capable of estimating connectivity at the cortical level by extracting the source component with the maximum temporal correlation between the activity of each targeted region and a reference signal. The spatiotemporal correlation dynamics can be obtained by applying SILSC at every brain region and with various time latencies. The results of six simulation studies demonstrated that SILSC is sensitive to detect the source activity correlated to the specified reference signal and is accurate and robust to noise in terms of source localization. In a facial expression imitation experiment, the correlation dynamics estimated by SILSC revealed the regions with mirror properties and the regions involved in motor control network when performing the imitation and execution tasks, respectively, with the left inferior frontal gyrus specified as the reference region. (C) 2015 Elsevier Inc. All rights reserved.
An Intelligent Analysis of Iddq Data for Chip Classification in Very Deep-Submicron (VDSM) CMOS Technology
(2012)
Chang, Chia-Ling (Lynn)
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Chang, Chia-Ching (Austin)
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Chan, Hui-Ling
;
Wen, Charles H. -P.
;
Bhadra, Jayanta
Iddq testing has been a critical integral component in test suites for screening unreliable devices. As the silicon technology keeps shrinking, Iddq values and their variation increase as well. Moreover, along with rapid design scaling, defect-induced leakage currents become less significant when compared to full-chip current and also make themselves less distinguishable. Traditional Iddq methods become less effective and cause more test escapes and yield loss. Therefore, in this paper, a new test method named sigma-Iddq testing is proposed and integrates (1) a variation-aware full-chip leakage estimator and (2) a clustering algorithm to classify chip without using threshold values. Experimental result shows that sigma-Iddq testing achieves a higher classification accuracy in a 45nm technology when compared to a single-threshold Iddq testing. As a result, both the process-variation and design-scaling impacts are successfully excluded and thus the defective chips can be identified intelligently.
Maximum Multiple-Correlation Beamformer for Estimating Source Connectivities in Electromagnetic Brain Activities
(2011)
Chan, Hui-Ling
;
Chen, Yong-Sheng
;
Chen, Li-Fen
;
資訊工程學系
;
Department of Computer Science
Synchrony is a phenomenon of local-scale and long-range integrations within a brain circuit. Synchronous activities manifest themselves in similar temporal structures that can be statistically quantified by temporal correlation. In previous studies, synchronous activities were estimated by calculating the correlation coefficient or coherence between a single reference signal and the activity in a brain region. However, a brain circuit may involve multiple brain regions and these regions may communicate to each other through different temporal patterns. Therefore, temporal correlation to multiple reference signals is effective in quantify the source connectivities in the brain. This paper proposes a novel algorithm to calculate the maximum multiple-correlation for each brain region which has an activity estimated by a beamformer. Furthermore, this algorithm can accommodate various latencies of activities in a circuit. Experimental results demonstrate that the proposed method can accurately detect source activities correlated to the given multiple reference signals, even when unknown latencies exist between the source and references.

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